{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4a6b887b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "\n",
    "# скроем предупреждения о возможных ошибках для лучшей читаемости\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "87c71221",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('insurance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7ac66185",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.drop(index = list(range(data.shape[0] // 16, data.shape[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "736cbde1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400\n",
       "1   18    male  33.770         1     no  southeast   1725.55230\n",
       "2   28    male  33.000         3     no  southeast   4449.46200\n",
       "3   33    male  22.705         0     no  northwest  21984.47061\n",
       "4   32    male  28.880         0     no  northwest   3866.85520"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "adf1b24a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.900</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.770</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.000</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.705</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.880</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>39.805</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2755.02095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>41</td>\n",
       "      <td>female</td>\n",
       "      <td>32.965</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>6571.02435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>26.885</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>4441.21315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>45</td>\n",
       "      <td>female</td>\n",
       "      <td>38.285</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>7935.29115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "      <td>37.620</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>37165.16380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     sex     bmi  children smoker     region      charges\n",
       "0    19  female  27.900         0    yes  southwest  16884.92400\n",
       "1    18    male  33.770         1     no  southeast   1725.55230\n",
       "2    28    male  33.000         3     no  southeast   4449.46200\n",
       "3    33    male  22.705         0     no  northwest  21984.47061\n",
       "4    32    male  28.880         0     no  northwest   3866.85520\n",
       "..  ...     ...     ...       ...    ...        ...          ...\n",
       "78   22  female  39.805         0     no  northeast   2755.02095\n",
       "79   41  female  32.965         0     no  northwest   6571.02435\n",
       "80   31    male  26.885         1     no  northeast   4441.21315\n",
       "81   45  female  38.285         0     no  northeast   7935.29115\n",
       "82   22    male  37.620         1    yes  southeast  37165.16380\n",
       "\n",
       "[83 rows x 7 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna(axis=1, how='any')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "13f365ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age - int64 - 0\n",
      "sex - object - 0\n",
      "bmi - float64 - 0\n",
      "children - int64 - 0\n",
      "smoker - object - 0\n",
      "region - object - 0\n",
      "charges - float64 - 0\n"
     ]
    }
   ],
   "source": [
    "for col in data.columns:\n",
    "    null_count = data[data[col].isnull()].shape[0]\n",
    "    if null_count == 0:\n",
    "        column_type = data[col].dtype\n",
    "        print('{} - {} - {}'.format(col, column_type, null_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d89ec4d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "smoker\n",
      "region\n"
     ]
    }
   ],
   "source": [
    "for col in data.columns:\n",
    "    column_type = data[col].dtype\n",
    "    if column_type == 'object':\n",
    "        print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3f84e833",
   "metadata": {},
   "outputs": [],
   "source": [
    "le1 = LabelEncoder()\n",
    "data['sex'] = le1.fit_transform(data['sex']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "36509a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "le2 = LabelEncoder()\n",
    "data['smoker'] = le2.fit_transform(data['smoker']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0e3fbb22",
   "metadata": {},
   "outputs": [],
   "source": [
    "le3 = LabelEncoder()\n",
    "data['region'] = le3.fit_transform(data['region']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "aa403a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in data.columns:\n",
    "    column_type = data[col].dtype\n",
    "    if column_type == 'object':\n",
    "        print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e5f9115f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "data_x = data.loc[:, data.columns != 'age']\n",
    "data_y = data['age']\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(data_x, data_y, test_size=0.3, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "21ee3c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58, 6)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "112bbc06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 6)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9b7603bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import median_absolute_error, r2_score\n",
    "\n",
    "def test_model(model):\n",
    "    print('mean_absolute_error: {}'.format(round(mean_absolute_error(test_y, model.predict(test_x)), 2)))\n",
    "    print('median_absolute_error: {}'.format(round(median_absolute_error(test_y, model.predict(test_x)), 2)))\n",
    "    print('r2_score: {}'.format(round(r2_score(test_y, model.predict(test_x)), 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cc5def6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=80)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=80)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(n_estimators=80)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "ran_80 = RandomForestRegressor(n_estimators=80)\n",
    "ran_80.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "61153bde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error: 5.44\n",
      "median_absolute_error: 3.75\n",
      "r2_score: 0.8\n"
     ]
    }
   ],
   "source": [
    "test_model(ran_80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f5501dca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'n_estimators': array([ 50,  60,  70,  80,  90, 100, 110, 120, 130, 140, 150, 160])}]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_range = np.arange(50, 170, 10)\n",
    "tuned_parameters = [{'n_estimators': param_range}]\n",
    "tuned_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f48eaa5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=None, test_size=None, train_size=None),\n",
       "             estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid=[{&#x27;n_estimators&#x27;: array([ 50,  60,  70,  80,  90, 100, 110, 120, 130, 140, 150, 160])}],\n",
       "             return_train_score=True, scoring=&#x27;r2&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=None, test_size=None, train_size=None),\n",
       "             estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid=[{&#x27;n_estimators&#x27;: array([ 50,  60,  70,  80,  90, 100, 110, 120, 130, 140, 150, 160])}],\n",
       "             return_train_score=True, scoring=&#x27;r2&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=None, test_size=None, train_size=None),\n",
       "             estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid=[{'n_estimators': array([ 50,  60,  70,  80,  90, 100, 110, 120, 130, 140, 150, 160])}],\n",
       "             return_train_score=True, scoring='r2')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "gs = GridSearchCV(RandomForestRegressor(), tuned_parameters,\n",
    "                  cv=ShuffleSplit(n_splits=10), scoring=\"r2\",\n",
    "                  return_train_score=True, n_jobs=-1)\n",
    "gs.fit(data_x, data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4b7a15cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = gs.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "467a84b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(param_range, gs.cv_results_[\"mean_train_score\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dd7a3615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+DBtuHZIigjAlJRyiCHxSZJA6HCLyMiYso8DGW/InXd02HK5qBgDMkFHDbW+OIXLbjzFhIQo0TFhGofeIfjbekq87Vt0Cc7cNkcFqZMaESB3OgBx9LF+dbEBLh0XiaIjIm5iwjML4hFColQKa2y2oamLjLfk2uRx4OJTM2FCMjw9Ft03EZ8e5ykIUSJiwjIJWpTzfeMuyEPk4x/lB02XYv9LbwknnzxYiosDBhGWUcpLYeEu+TxTF8xNuZbhDqDdHwrK7pB4dXVaJoyEib2HCMkrcKUT+oKqpA3UmM1QKAVNSI6QOZ0iTkvRIiQxCp8WG3SX1UodDRF7ChGWUeu8UYuMt+SpH/8qk5HDo1EqJoxmaIAgcIkcUgJiwjNKEhDCoFAKa2i0428zGW/JNjoRlpsz7VxwchyF+WmxAV7dN4miIyBuYsIySTq3E+Hg23pJv671DyBdMT4tETKgWps5u7D11TupwiMgLmLC4Qe+DEIl8Tau5G8dr7d+7vpKwKBQCFkyKB8DdQkSBggmLG+SksPGWfFdBRTNsIpASGYR4vU7qcIbN0cfySZEBVhv7x4j8HRMWN2DjLfmyA2caAfjO6orDJZnR0OtUaGg1O2fIEJH/GlHC8txzzyEjIwM6nQ6zZ8/G/v37h/W6zZs3QxAELF++vM/9ra2tuOeee5CSkoKgoCBkZ2dj48aNIwlNElk9jbfn2rpQ08JTZMm3+FrDrYNGpUDeRJaFiAKFywnL66+/jnXr1uHRRx/FwYMHMWXKFCxcuBB1dXVDvq68vBz3338/5s2b1++xdevWYdu2bXjllVdQXFyMn/3sZ7jnnnuwZcsWV8OThE6txLiexluWhciXWG0iDlU0A5D/hNuBLOg19Zarm0T+zeWE5emnn8add96JtWvXOldCgoOD8eKLLw76GqvVilWrVuGxxx5DZmZmv8e/+uorrFmzBldeeSUyMjLwwx/+EFOmTBn2yo0c5CbrAXCnEPmWEoMJreZuhGiUyErQSx2Oy64YHwudWoGzzR04Vs2mdyJ/5lLC0tXVhfz8fOTl5Z1/A4UCeXl52Lt376Cve/zxxxEXF4c77rhjwMcvvfRSbNmyBWfPnoUoiti5cydKSkqwYMECV8KTFCfeki9ylIOmpUVCqZDngYdDCdIoceX4OAAcIkfk71xKWBoaGmC1WhEfH9/n/vj4eNTWDvzDYs+ePXjhhRewadOmQd/32WefRXZ2NlJSUqDRaLBo0SI899xzuPzyywd8vtlshtFo7HOTWg4bb8kHORIWXywHOTiGyLGPhci/eXSXkMlkwurVq7Fp0ybExMQM+rxnn30WX3/9NbZs2YL8/Hz86U9/wo9//GN8+umnAz5/w4YNCA8Pd95SU1M99SUMW3aiHkqFgIbWLtQa2XhLvsFXG257m58VB5VCQGldK07WtUodDhF5iMqVJ8fExECpVMJgMPS532AwICEhod/zy8rKUF5ejqVLlzrvs9nsY7RVKhVOnDiBpKQk/PKXv8Q777yDa6+9FgAwefJkFBQU4I9//GOf8pPD+vXrsW7dOud/G41GyZMWnVqJcXGhOF5rQmFVCxLDgySNh+hC6kydqGhshyAAU9MipA5nxMKD1Lh0bAw+L6nH9mO1GBs3VuqQiMgDXFph0Wg0mDFjBnbs2OG8z2azYceOHZgzZ06/52dlZaGwsBAFBQXO27JlyzB//nwUFBQgNTUVFosFFosFCkXfUJRKpTO5+TatVgu9Xt/nJge9y0JEcnewZ3VlQnwY9Dq1xNGMDg9DJPJ/Lq2wAPYtyGvWrMHMmTNx8cUX45lnnkFbWxvWrl0LALjtttuQnJyMDRs2QKfTIScnp8/rIyIiAMB5v0ajwRVXXIEHHngAQUFBSE9Px+7du/Hyyy/j6aefHuWX5125yeH4b34VG2/JJ/ja+UFDuSY7Hr96txBHqlpwtrkDyRFc4STyNy4nLCtXrkR9fT0eeeQR1NbWYurUqdi2bZuzEbeioqLfasmFbN68GevXr8eqVavQ2NiI9PR0/O53v8Ndd93laniScq6wcHsl+YADjv6VDN9PWGLDtJiVHoX95Y34+Fgt1l42RuqQiMjNBNEPtrQYjUaEh4ejpaVF0vJQR5cVkx7dBpsI7Pvl1T51LgsFlk6LFbn/ux0Wq4jPH5iPtOhgqUMatf/74hR++2ExZo+Jwuv/079ETUTy48rvb54l5EZBGiXGxfVMvK1iWYjkq/BsCyxWETGhWqRG+Uf5ZGFPH8s35Y1oaDVLHA0RuRsTFjfjADnyBb23MwuC7w2MG0hqVDBykvWwicCnRYYLv4CIfAoTFjfjiH7yBf7UcNsbdwsR+S8mLG6Wm8IVFpI3URSdW5pn+EHDbW+OqbdfnjwHY6dF4miIyJ2YsLjZxEQ9FAJQZzKjjhNvSYbKz7XjXFsXNCoFJiXJY4aRu4yNC0NmbAi6rDbsPD70CfJE5FuYsLhZsEaFi2JDAXCVheTpQHkjAGBycji0KqXE0bgfy0JE/okJiwfksvGWZOxghX+WgxwcZaGdx+vRabFKHA0RuQsTFg/giH6SswPlPQlLmn8mLLnJ4UgK16HDYsUXpQ1Sh0NEbsKExQPYeEty1dJuQWnPicb+tkPIQRAELOxZZdl2lGUhIn/BhMUDshP1EATAYDSjzsTGW5KPg5X21ZUxMSGIDtVKHI3nOIbIfVpsgMU68CGqRORbmLB4QIj2fOPtsbM8V4jkI7/cP+evfNusjChEh2jQ0mHBvlONUodDRG7AhMVD2HhLcuSvA+O+TakQcE22/UBW7hYi8g9MWDyEI/pJbixWGwoqmwH4f8ICwNnHsv1YLWw2nz/jlSjgMWHxkFzuFCKZOV5jQofFCr1OhbE9JUt/dulF0QjTqlBnMuNQT6JGRL6LCYuHZCfZG29rWjp5cizJQv4Zey/H9PRIKBT+ceDhULQqJeZnxQFgWYjIHzBh8ZBQrQpjYkIAsCxE8nCg1wnNgWJRr+3NosiyEJEvY8LiQc6yUBUTFpKe48DD6QGUsFwxPhZalQIVje04XmuSOhwiGgUmLB7EnUIkF9XNHahu6YRSIWBqaoTU4XhNiFaFy8fHAuAQOSJfx4TFgzii33eIooiH3jqCn79xGFY/3FHi2M6cnahHsEYlcTTexcMQifwDExYPmpSkBwBUt3TiHBtvZa2ysQObv6nEWwer8M8vT0sdjtsFyvyVgVw9MQ5KhYDjtSacbmiTOhwiGiEmLB4UplMjs6fx9mg1J97KWVHN+f9//vRxCSob2yWMxv0COWGJCNZgTmY0AK6yEPkyJiwexrKQb+idsHRYrPjlO4V+s6ukvavb+fUFYsIC9B0iR0S+iQmLhzkbb7lTSNaKelbAbpuTDo1KgS9KG/D2wbMSR+UeBZXNsNpEJIXrkBQRJHU4kliYHQ9BAA5VNKO2hQeSEvkiJiwexhH9vqG4ZwViSW4ifpY3DgDwmw+L/GLoXyBuZ/62OL0O03p2R31cxFUWIl/EhMXDJiXbG2/PNnegqa1L4mhoIC3tFpxt7gAATEzU4855mZiYqEdzuwWPv18kcXSjdyCA+1d66z1Ejoh8DxMWD9Pr1Jx4K3PFtfbVlZTIIIQHqaFWKvDkilwoBGDL4Wp8dtwgcYQjZ7OJzhWWmelREkcjrYU925v3nW7kHw9EPogJixewLCRvjv6ViYl6532TUyJwx9wxAIBfv3MUreZuSWIbrbL6Vhg7uxGkViIrMUzqcCSVHh2CiYl6WG0iPi323SSUKFAxYfGCnJ55LNwpJE+OHTTZvRIWALjvmvFIjQpCdUsn/rj9hBShjZqjHDQ1NQJqJf+5c4gcke/iTzAv4Ih+eXM03GYn9U1YgjUq/P76XADAS3vLnbNMfEkgz18ZyMKceADA56UNPrtqRhSomLB4waSehKWqiY23ctPVbUOpoRVA/xUWAJg3LhYrpqdAFIGH3jqCrm6bt0McFSYsfU2ID0NGdDC6um3YdaJO6nCIyAVMWLwgPEiN9OhgAMDRaq6yyElZfSu6rDaEaVVIiRx4Rsmvr52I6BANSuta8fyuMi9HOHLnWs3OUfTT05iwAIAgCM4hctwtRORbmLB4yfmJtxzRLyeOctDERD0EQRjwOZEhGjy6bBIA4G87S1FqMHktvtE4WNEMABgXF4rwYLW0wciIo49l5/E6dFqsEkdDRMPFhMVLcjmiX5YcO4S+3b/ybUsnJ+LqrDhYrCIefOsIbD5wovOBM40AgJkZXF3pbUpKBBLDdWjrsuKL0gapwyGiYWLC4iVsvJUnxwyWgfpXehMEAb9ZnoMQjRIHK5rxyr4z3ghvVJwTblkO6kOhEJxD5LYW1kgcDRENFxMWL8lJsicsFY3taGm3SBwNAYAoigPOYBlMUkQQHlycBQB4cutxVPdMx5Ujc7cVh3vOr5qZEdgD4wayJDcRAPBJsQHmbpaFiHwBExYvCQ9WIy2KjbdyUmvsRFO7BUqFgHHxocN6za2z0zE9LQJtXVY8/O5R2Z7ofKzaiK5uG6JCNMjoafim82akRSIuTAtTZze+OnlO6nCIaBiYsHgRy0Ly4mi4HRsbCp1aOazXKBQCnlwxGWqlgB3H6/DBEXmWFPLLz5eDBmsmDmS9y0IfsSxE5BOYsHgRR/TLy/lykGsj68fFh+HH88cCAP53yzFZztZxzF9hw+3gFufYy0IfFxlgsfrWfB2iQMSExYtykjmiX06KBplwOxx3X3kRxsWF4lxbF373UbG7QxsVURSRX8GBcRdy8ZgoRIdo0NJhwd4yloWI5I4Jixc5Gm/PnGtHSwcbb6VWXGOfp5KdGO7ya7UqJZ5YMRmCAPw3vwp7ZLQ9trKxA/UmM9RKwVmGpP6UivND5LYeZVmISO6YsHhRZIjGOU31GFdZJNVm7kb5OfsUWFdLQg4z0iNx2yXpAID17xxBR5c8dpvkV9jnr+Qkhw+7NydQLekpC20/ZkA3y0JEssaExcvYeCsPx2tNEEUgXq9FdKh2xO/zwKIsJIXrUNnYgT9/WuLGCEfuQE/D7QzOX7mg2ZlRiAxWo7GtC/tPN0odDhENgQmLl7HxVh6KaoY/f2UooVoVfnt9DgDg/744hSNVzaMNbdTYcDt8aqUCC7J7dguxLEQka0xYvMyxwnKsmmcKSck5kn+UCQsAXJUVj2VTkmATgQffKpR0x4mp04ITPWcdTWfD7bAsznUchmiA1QeOXCAKVExYvMyRsJxuaIOxk423UikexQ6hgTyyNBsRwWoU1xix6YtTbnnPkThU0QxRBNKighEXppMsDl9y6UUx0OtUaGg140A5y0JEcsWExcsiQzRIjnA03nKVRQpWm4jjte4pCTnEhGrx8LXZAIBnPi3F6YY2t7yvqxzlIG5nHj6NSoEFkxy7hWoljoaIBsOERQI8uVlapxva0GmxIUitREZ0iNve94bpyZg3LgZd3TY8JNGJzkxYRmZJ7vntzb5wEjdRIGLCIoHcFDbeSslRDspKDINS4b6x9YIg4PfX5yJIrcS+041440Cl2957OKw2EYc4MG5ELhsbgzCtCgajGQd7riERyQsTFgnkcIVFUu7aITSQ1Khg/HzBeADA7z4qRp2x0+2fMZjjtUa0dVkRplVhfPzIZssEKq1KibzseADAR4UsCxHJERMWCeT0NHqeamiDiY23XufOHUIDuf3SDExOCYepsxuPbjnmkc8YyMGectDUtAi3rhwFisU5LAu5QhRF/HVHKZ7beZJD98grmLBIIDpUi6Rw+w4Obm/2vmIPrrAAgEqpwBM3TIZKIWDr0Vps81IjJ/tXRufy8bEI0ShR09KJwzKYpyN3Byua8PQnJfjD9hO49YV9aGg1Sx0S+TkmLBJhWUgaDa1m1JnMEAQgK8FzZZPsJD3+54pMAMAj7x31ytlRBxwD49KjPP5Z/kinVuKqifayEHcLXdjr35zv0fr6VCOWPrsHBZXN0gVEHiOKIsob2vBVmbRnpjFhkQhH9EvDsboyJjoEIVqVRz/rJ1eNQ2ZMCOpMZjyx9bhHP8tg7ERVUwcUAjAllQcejtSSnrLQR4U1EEWWhQbTau7GB0fsk4E33JCLzNgQ1LR04qaNe7F5f4XE0dFonWs147PjBjz9SQnWvLgf037zCa784y7c859Dkv678OxPbBpUDncKScLRv+KpclBvOrUSv78hFzf/42u8tr8C101NwiWZ0R75LEc5aEKCHmE6tUc+IxBcOSEOQWolqpo6cPSs0bmjj/r66EgN2rusyIwJwc2zUvGdyYn4+RuH8XGRAQ+9XYjDVc3432WToFXx8E256+iy4lh1Cwoqm1FQ2YzDVc2obOzo9zyNSoH06GAYO7sRHiTNzxgmLBLpPfG21dyNUA//tU92RW6ecHshl2RG45aL0/Da/gqsf7sQW++d55ETlJ3nB7F/ZVSCNErMz4rFR4W1+OhoDROWQbzes2X/xpmpEAQBYTo1Nt46A8/vLsMfPz6B1/ZXoqjGhOdXTUdSz6BMkp7VJqKsvhUFFc0oqGrG4cpmHK81DXgkxUWxIZiSGoFpqRGYkhqBrAQ9NCppizL8LSmRmFAtEsN1qGnpRFG1ERePYd+BNzhH8nthhcXhocVZ2FFswOmGNjz7WSkeWJjl9s9gw637LM5JxEeFtdhaWINfLJwAQeCOq95O1pmQf6YJSoWAFTOSnfcrFAJ+PH8scpLDce/mQzhc2Yylz+7Bs9+bhksvipEw4sBV09KBw5XNOFRpT04Kq1rQ1mXt97zYMC2mpkZgamoEpqREIDclXLJVlKEwYZFQTnI4alo6UXi2hQmLF3RarCirt4/M90ZJyCE8SI3Hr8vBXa/k4++7T+Ha3CS3rvB0WuxLugATFneYnxUHrUqB8nPtKK4xeW01zle8caAKADB/QtyA51VdMT4W798zF//z73wU1Rix+oX9WL84C3fMHcPkz4NMnRYcqbKXdg73lHYMxv47t4I1SuQmh59PUFIjkBiu84n/b5iwSCg3ORyfFBm4U8hLSgz2pc+oEA3i9VqvfvainAQsmpSAbcdq8dDbR/DOjy5z26yUI1UtsFhFxIVpkRLJ5ffRCtWqcMX4WHxcZMDWozVMWHqxWG14+6A9YblpZsqgz0uNCsZbd1+KX71TiLcPncVvPyxGQWUznlwx2ePN7oGgq9uGE7UmFFQ1o6DCnpyU1bfi2/2wSoWA8fFhPclJOKakRmBcnHsnfHsTv3MkxJ1C3tW7HCTFXxOPXTcJX5Y14EhVC/755Wn8YF6mW973wBn7CcMzMyJ94q8kX3Dt5ER8XGTAh4U1WHfNeF7XHp8dr0NDaxdiQrWYnxU35HODNEr86aYpmJoWgcffL8IHR2pQYjDh76tnYkyM+87w8neiKKKisf18U2xlM45WG9HV3X9YX0pkEKakRmBqSgSmpkVgUpIewRr/+TXvP1+JD3LMYimrb0WbuZt/eXjY+R1C0oytj9fr8MslE7H+7UL86eMSLJyUgNSo4FG/r2PC7fQ0loPc5aqsOGiUCpyqb0NpXSuPOujxRs/slRUzkqFWXrgBUxAE3DYnA9mJetz96kGUGFqx7Nk9+PPKqc6jEKivc61mZ2nHsWunub3/HCe9TtWnKXZySgRiw7y7cuxtI2r5fe6555CRkQGdTofZs2dj//79w3rd5s2bIQgCli9f3ud+QRAGvP3hD38YSXg+IzZMiwS9DqJ4fvcKeU5xjQmA93YIDeTmWam4JDMKHRYrfvlO4ahnGoiiyIZbDwjTqXH5eHuj6Ic980YCncHYiZ0n6gAAN81Mdem1MzOi8OFP5mJmeiRM5m784OUDePqTEh6BAMDcbcXr31TgJ68dwuVP7cSM336Ktf/6Bn/ZUYrdJfVobrdAo1RgamoEbr80A39eOQU7778Shx9dgH/fMRvrFkzA1RPj/T5ZAUawwvL6669j3bp12LhxI2bPno1nnnkGCxcuxIkTJxAXN/gSYXl5Oe6//37Mmzev32M1NX1/IGzduhV33HEHVqxY4Wp4PicnWY9aYycKq1owK4ONt55is4kePfRwuARBwIYbJmPhM5/ji9IGvHPoLG6YPngvwIWcamhDU7sFWpUCk5K4BdedFuck4tPiOmw9WoP7rhkvdTiSe+tgFWwiMCsjEhfFhrr8+ji9Dv+58xL87sMivLT3DP66oxSFVc14ZuU0hAfLb0eKp3VbbXj74Fn8ZUcpzjb3nXuSGRuCqSn2lZOpqRHISgzjTBuMIGF5+umnceedd2Lt2rUAgI0bN+LDDz/Eiy++iIceemjA11itVqxatQqPPfYYvvjiCzQ3N/d5PCEhoc9/v/fee5g/fz4yM91T45eznORwfFpcx8ZbD6tq6kCruRsapWJEP2zdaUxMCH6WNw5PbTuBxz8owuXjYxETOrK/jhyrK1NSIiSfkeBv8ibGQ60UUGJoxck6E8bGBW5ZSBRFvNmzO+hGF1dXetOoFHjsuhxMSY3A+rcLsfNEPZb+bQ/+vnqGpH9IeJPVJuKDI9X48yclKD/XDgCIC9Pie7PTMCM9EpOTIwIygRsOl37CdXV1IT8/H3l5eeffQKFAXl4e9u7dO+jrHn/8ccTFxeGOO+644GcYDAZ8+OGHw3quP2DjrXc4VlfGJ4QOq/buaXfOy8TERD2a2y14/P2iEb9PfnlPOSiD5SB3Cw9W47Kx9rLQ1sLAPlto/+lGnG5oQ4hGiWtzE0f9fjdMT8Fbd1+KlMggVDS24/r/9yXeKzjrhkjlSxRFbDtag8V/+Rz3bi5A+bl2RIVo8KslE7H7gfn4Wd54zBsXy2RlCC795G5oaIDVakV8fN9mqfj4eNTWDvwPes+ePXjhhRewadOmYX3GSy+9hLCwMNxwww2DPsdsNsNoNPa5+arcXo237V3dEkfjv5zloAR5/BWnVirw5IpcKARgy+FqfHbcMKL3ya/oSVjYcOsRS3Lsv5w/CvDDEB2TbZdOSXLb5oCc5HC8f89cXD4+Fp0WG+7dXIDH3y+Cxdp/94svE0URO4/XYenf9uCuV+yNx3qdCvcvGI/PfzEfd16eiSANyz3D4dE/NU0mE1avXo1NmzYhJmZ4kw5ffPFFrFq1Cjpd/4FEDhs2bEB4eLjzlpo68iVKqcXpdYgL08Imnt92S+7n2CEkp5kak1MicMfcMQCAX79zFK1m1xLW5vYunKxrBQBMZ8OtR1yTHQ+lQkBxjRGnG9qkDkcSxk4LPiq09xneNMu9P2sjQzT45+2zcM/8sQCAF788jVX/tw/1pv4Dz3zRVycbsOL5r7D2X9/g6FkjQjRK/OSqsfjiwatwz1XjeCSLi1xKWGJiYqBUKmEw9P1r0GAw9OtDAYCysjKUl5dj6dKlUKlUUKlUePnll7FlyxaoVCqUlZX1ef4XX3yBEydO4Ac/+MGQcaxfvx4tLS3OW2Vl5ZDPlztnWaiKZSFPkWIk/3Dcd814pEYFobqlE3/cfsKl1x7sWV3JjA1BVIjGE+EFvMgQDS69yH5g5dajgblb6IPDNei02DA2LhTTUiPc/v5KhYD7F07A31fPQKhWhf2nG/GdZ79wfn/7ovwzjbjlH1/je/+3DwcrmqFVKfDDyzPx+S/m4+cLJshy7L0vcClh0Wg0mDFjBnbs2OG8z2azYceOHZgzZ06/52dlZaGwsBAFBQXO27JlyzB//nwUFBT0Wxl54YUXMGPGDEyZMmXIOLRaLfR6fZ+bL8tx9rFwhcUTWtotzi78LJklLMEaFX5/fS4A4KW95c4m2uFwbmdmOcijFveUhQK1j8VRDlrZc9ChpyyclID37rkMY+NCYTCasfLve/HK12dGvfXfmwqrWnD7P/djxfN7sffUOaiVAtbMSccXv5iPXy6ZiOgRNteTncsloXXr1mHTpk146aWXUFxcjLvvvhttbW3OXUO33XYb1q9fDwDQ6XTIycnpc4uIiEBYWBhycnKg0Zz/q9BoNOLNN9+84OqKP3KssHCnkGcU19oTwZTIIFn+ZTNvXCxWTE+BKAIPvXVkwAmWAznQ03A7kw23HrVgUjwUgr0xvrKxXepwvOpErQmHK5uhUgi4fnryhV8wShfFhuLdH1+GxTkJsFhF/Prdo/jFf4+g09L/wD45OVFrwl3/zsfSv+3BrhP1UCoE3DwrFTvvvxKPXZeDOP3gLQ40fC4X0FauXIn6+no88sgjqK2txdSpU7Ft2zZnI25FRQUUCtdbYzZv3gxRFHHLLbe4/Fpf5zjCvrTOhI4uKxuw3MzZvyKz1ZXefn3tROw6UYfSulY8v6sM9+aNG/L5FqsNh6uaAXBgnKfFhGoxe0w09p46h61Ha/DDyy+SOiSveb1nsm3exPgRb713VahWhf+3ajr+/vkpPLXtON7Mr8LxWhOev3U6UiJHPxnanU43tOGZT0uw5XA1RBEQBGD51GTce/U4ZPD4AbcbUdPtPffcgzNnzsBsNmPfvn2YPXu287Fdu3bhX//616Cv/de//oV333233/0//OEP0d7ejvDwwBt+Fa/XIban8ZYTb91PDgPjLiQyRIP/XTYJAPC3naUoNZiGfH5RtRGdFhsigtXIjJF2rkwgWJJr79H7KIDKQl3dNrxzqOegw1kjH244EoIg4K4rLsLL35+NyGA1Cs+2YOmze7CntMGrcQymsrEdv/jvYeQ9vRvvFdiTlSW5Cdj+s8vx55VTmax4iPQDKQgAy0Ke5Gy4ldEOoYF8Z3Iirs6Kg8Uq4qG3C4ccW57f6/wghY+evOpLFuYkQBCAgsrmflNJ/dWnxQY0tVsQr9fi8nGxksQwd1wM3v/JXOQk69HUbsFtL+7Dxt1lkvW1GIydePjdo7jqT7vwxoEqWG0irsqKwwc/mYv/t2oGz5zyMCYsMpHDAXIe0dVtQ6nBvvVXziUhwP5X5W+W5yBEo0T+mSa8su/MoM91zl9hOcgr4sJ0zqMztgXITBZHOei7M1KgknDYYkpkMP5716X47owU2ETgia3H8aNXD7o8BmA0GlrN+O0HRbj8qZ3499dnYLGKuGxsNN66+1K8ePss589v8iwmLDKR0/PXP1dY3KusvhVdVhvCtCqkRAZJHc4FJUUE4cHFWQCAJ7ceR/UAf82Lonh+wi0TFq9ZkmMvC20t9P/tzdXNHfi8tB4AcOMM6edc6dRK/OG7k/Hb5TlQKwVsPVqL5c99ibL6Vo9+bku7BX/YfhyXP7UT/7fnNMzdNsxMj8Rrd16CV39wCf/9eRkTFpk433jbKvuOeF/iKAdNTNJ7dEumO906Ox3T0yLQ1mXFw+8e7bf8Xd3SiVpjJ1QKAVNSIqQJMgAt6tnefOBME2pbOiWOxrP+m18FUQRmj4mSTT+GIAi49ZJ0bP7hHMTrtThZ14rr/vYlth9z/4pXq7kbz+4oxdynPsNzO8vQ3mVFbnI4/rV2Ft68aw7m9MzmIe9iwiITCXodYkI1sPY6VZhGzxd2CH2bQiHgyRWToVYK2HG8Dh8c6fsX/YHyRgDApCQ9d5R5UUK4zvkX9TY/HiJns4l4M79n9oqbJ9u6w4z0SLz/k7m4eEwUWs3d+J9/5+OP20/AOkTP13B1dFnxj8/LMO/Jz/CnT0pg6uzGhPgw/H31DGy55zJcOSHOZ/7w8UdMWGRCEARnHfQYy0JuUyTTCbcXMi4+DD/uGVf+v1uOoamty/nYQUfDLZejvW5xT1nIn88W+vrUOVQ2diBMq3IOzZObuDAdXv3BbHz/MvvRFn/beRJr//VNn38nrjB3W/HSV+W4/A878fuPjqOp3YLMmBD89ZZp2HrvPCyclMBERQaYsMgIT252L1EUfWaH0EDuvvIijIsLxbm2Lvzuo2Ln/QfOsH9FKot7Tir+prwRdSb/LAs5Jtsum5ok6xU8tVKBR5Zm4y83T4VOrcDnJfVY+rc9LvUBWqw2bN5fgfl/2IVHtxxDvcmMlMgg/OG7k/HxfZdj2ZQk7sKTESYsMsIR/e5Va+xEU7sFSoWAsXG+N6tEq1LiiRWTIQj2noI9pQ1oM3c7kzAmLN6XHBGEKakREEVg+7GRnbAtZy3tFmztWT2SYzloINdNTcY7P7oMaVHBqGrqwIrnv8LbB6uGfI3VJuKdQ1XIe3o3Hnq7ENUtnYjXa/Hb5Tn47OdX4saZqZLujKKB8f8RGXGssJQaTGy8dQPHL/axsaHQqeX7l+JQZqRHYs2cDADA+neO4OtT52AT7b84E8Plv+vJH/nzbqEth8+iq9uGrIQw588jXzAxUY/375mLKyfEwtxtw7o3DuPR9472O+bCZhPxUWENFj7zOe57/TDOnGtHdIgGv752InY/MB+3XpIOjYq/FuWK/8/ISGK4DtEhGnTbRByvHXrSKV2Ys+HWB8tBvd2/cAKSwnWobOzAg28VAuDqipQcfR1fnzqHc61miaNxL0c56CYPH3ToCeHBary4ZhZ+erX9WIuX9p7B9zZ9jTpjJ0RRxI5iA77z7B786NWDOFnXivAgNR5YOAGf/2I+fjAv02f/qAkkTFhkpHfjLftYRu/8SH7fnj4ZqlXht9fnALAPsAKYsEgpLToYOcl62ETg4yL/KQsdq27B0bNGaJQKXD/N8wcdeoJCIWDdNePxf7fNRJhWhQNnmnDts3tw/f/7Cne8dABFNUaEalX46dXj8MWD8/Hj+WMRonX5SD2SCBMWmXGO6K9iwjJaxTX2VarsRN9Z2h7MVVnxWDYlyfnfTFik5Vhl+ciPykJv9Ey2vWZSPCJDNBJHMzp52fHY8pO5GB8finqTGQWVzdCpFfifKzLx+S/mY90146HXye/kdhoaU0uZ4QqLe7Sau1F+rg2A76+wODyyNBvflDdCq1IgK8E/viZftTgnAX/YfgJflZ1DU1uXz/+C77RY8W5BNQB7OcgfjIkJwTs/ugx/+rgEaqWAO+aNQVyYTuqwaBSYsMhMTrK936Kkp/GWddWROVFrhCgC8XotokO1UofjFjGhWuz4+RVQKgTuYJBYZmwoshLCcLzWhE+KDT7/S/7jIgNaOixICtdh7tgYqcNxmxCtCo8szZY6DHIT/tSTmeSIIEQGq9FtE3GCjbcjVuQsB/l2w+23BWtU0KqYxMrBkp6ZLP6wW8hRDvruzFQoOXeEZIoJi8yw8dY9HDuEJvpZwkLy4UhY9pxsQEuHReJoRq6ysR17TjYAAG6ckSJxNESDY8IiQ87GWyYsI+bLE27JN4yNC8X4+FBYrPYts77qzXz7kLXLxkYjNSpY4miIBseERYacCUs1E5aRsNpEHK/1zTOEyLf4+m4hq03Ef3vNXiGSMyYsMuQoCZ2oNcHczYm3rjrd0IZOiw1BaiXSo0OkDof8mKMs9HlJA0ydvlcW+vJkA6pbOhEepMbCSQlSh0M0JCYsMpQSGYSIYDUsVhElta1Sh+NzHOWgrMQwNhCSR42PD0VmbAi6rDZ8drxO6nBc5phsu3xqEnckkuwxYZEhQRB4cvMonJ9wy3IQeZYgCFjio2WhprYufNJzgOONLAeRD2DCIlPcKTRyzjOEmLCQFyzOtZdSdp2oR5u5W+Johu/dgrPostowKUnv/HlDJGdMWGSKO4VGjjuEyJuyE/VIjw6GuduGnSd8oywkiiJe75m9snIWV1fINzBhkancXo233z4inQbX0GpGnckMQQDH15NXCILg3C20tbBW4miGp/BsC47XmqBRKXDdFN886JACDxMWmUqJDEJ4kBpdVhtKDJx4O1yO1ZUx0SEI1vDkCfKOJT1loc+O16GjS/47+xyrK4tzEhAezEMAyTcwYZEp+8Rbe0mDfSzD55xwy3IQeVFucjiSI4LQYbFid4m8y0IdXVZs8bODDikwMGGRMTbeus6xQ4gNt+RNgiA4V1k+knlZaNuxGpjM3UiNCsKczGipwyEaNiYsMsbGW9cVM2EhiSzuGSK3o9iATot8y0KOctCNM1Kh4Jwi8iFMWGTMkbAcrzHBYmXj7YV0Wqwoq28DwB1C5H1TUyKQGK5DW5cVX5Q2SB3OgM6ca8PXpxohCMB3edAh+RgmLDKWFhUMvU7FxtthKjGYYLWJiArRIC5MK3U4FGAUit67heQ5RO6Nnsm288bFIikiSOJoiFzDhEXG7I23LAsNV+9ykCBwqZu8z9HH8kmxQXbngFltIv7bczLzSjbbkg9iwiJzHNE/fM4dQomcv0LSmJ4WibgwLUyd3fjypLzKQp+X1MNgNCMyWI287DipwyFyGRMWmTu/U8gocSTyV8QJtyQxe1lInruFHM22109LgVbFgw7J9zBhkTnHCktxjZGNt0Ow2UQU19j7fLITeS4KScexW+jjY7WymVLd0GrGp8X2gw5vmsVmW/JNTFhkLj06GGE6Fbq6bSg1tEodjmxVNXWg1dwNjVKBzNgQqcOhADYrIwoxoRoYO7ux99Q5qcMBALxz8Cy6bSKmpIQjK4ErkOSbmLDInCAIyEli4+2FOMpB4xNCoVby25qko1QIWDjJXhaSw24hURSdu4Nu4kGH5MP4k90H5Kaw8fZCOOGW5GRJT1lo+7FadEtcyj1U2YzSulbo1AosnZIkaSxEo8GExQdMSuKZQhdyfocQExaS3uwxUYgMVqOp3YJ9pxsljeWNnmbbJTmJ0Ot40CH5LiYsPqB3463Uf63JFUfyk5yolApnWegjCctCbeZuvH+456BDloPIxzFh8QEZ0SEI1apg7rbhZD0bb7+tpd2Cs80dAHhKM8nH4l5lIatNlCSGjwpr0NZlRUZ0MGaPiZIkBiJ3YcLiAxQK4XxZqIploW9z9K+kRAZxyZtk49KLohEepEZDaxe+KZemLORotr1xZiqnP5PPY8LiI3hy8+BYDiI5UisVuCY7HoA0u4XK6lvxTXkTFDzokPwEExYfwZ1Cg3OssLDhluTm2p6y0NajtbB5uSzkWF25ckIc4vU6r342kScwYfERjhH9RWy87aeYI/lJpi4dG40wnQp1JjMOVjR57XMtVhveyj8LALiJBx2Sn2DC4iPG9DTedlpsKKtvkzoc2eg9AZglIZIbrUqJaybay0LePFto14l6NLSaEROqwdUTedAh+QcmLD5CoRCcKwgsC51XVt+KLqsNYToVUiKDpA6HqJ/FzrJQjdfKQo6DDm+YnsLJz+Q3+J3sQ9h4219xr/4V7oIgOZo3LgYhGiVqWjpRUNXs8c+rM3Zi54k6AMBNM9lsS/6DCYsPcSQsXGE5zzHhluUgkiudWomrJ3pvt9BbB8/CahMxPS0CY+PCPP55RN7ChMWH5CTbfykXVRslG0QlNzxDiHzBklzH1NtaiKLn/u2Koog3e3YHreRkW/IzTFh8yJiYUARrlOiwWFHGibcQRZE7hMgnXDE+DkFqJc42d3h0hfTAmSacamhDsEaJayfzoEPyL0xYfIiy18Rb9rEAtcZONLVboFIIGBsXKnU4RIMK0ihxVZZ9t44ndws5mm2vzU1EqFblsc8hkgITFh+Twz4WJ8fqykWxodCplRJHQzS0xT1loa1HazxSFjJ1WvDhEXuPDMtB5I+YsPgY7hQ6z9lwy3IQ+YD5E+KgVSlw5ly7s/fKnT48UoMOixWZsSGYkR7p9vcnkhoTFh/jSFiOsfG210h+7oQg+QvRqnDlhFgAwFYPlIVedzTb8qBD8lNMWHxMZqy98ba9y4rTDYHdeFtcYwIAZCeGSxwJ0fAs6Rki91Ghe8tCpQYTDlU0Q6kQcP30ZLe9L5GcMGHxMUqF4NzCG8h9LK3mbpSfsx9RwBUW8hVXZcVBo1LgVEMbSgzu+4PD0Wx7VVYc4sJ40CH5JyYsPsjZeFvl/jq4rzhRa4QoAvF6LaJDtVKHQzQsYTo1Lh9nLwt95KYhcl3dNrx9yH7Q4UoedEh+jAmLD2LjLVDkLAex4ZZ8y5Jeu4Xc4bPjBjS2dSEuTOvskSHyR0xYfFBuiqPxtsVrh6nJDXcIka+6emI81EoBJYZWnKwzjfr9HOWgFTNSoOJBh+TH+N3tg+xzRxRo67LiVEOb1OFIoqjXoYdEviQ8SI25Y2MAjH6IXG1LJ3aX1AMAbpzBgw7Jv40oYXnuueeQkZEBnU6H2bNnY//+/cN63ebNmyEIApYvX97vseLiYixbtgzh4eEICQnBrFmzUFFRMZLw/F7vxttALAtZbSJO1PIMIfJdi3vtFhqN/+ZXwiYCF2dEITOW057Jv7mcsLz++utYt24dHn30URw8eBBTpkzBwoULUVdXN+TrysvLcf/992PevHn9HisrK8PcuXORlZWFXbt24ciRI3j44Yeh07HbfTCBfHLz6YY2dFpsCFIrkR4dInU4RC5bkB0PlULA8VoTTo3wXDCbTcQbB6oAADdxsi0FAJcTlqeffhp33nkn1q5di+zsbGzcuBHBwcF48cUXB32N1WrFqlWr8NhjjyEzM7Pf47/61a+wZMkSPPXUU5g2bRouuugiLFu2DHFxca6GFzByArjx1jGSPysxDEoFB2SR74kI1mDORdEAgK1HR1YW2ne6ERWN7QjVqpyNvET+zKWEpaurC/n5+cjLyzv/BgoF8vLysHfv3kFf9/jjjyMuLg533HFHv8dsNhs+/PBDjB8/HgsXLkRcXBxmz56Nd99915XQAs75xltjwDXeOvpXWA4iX+YYIjfS3UJv9Ey2XTolEcEaHnRI/s+lhKWhoQFWqxXx8fF97o+Pj0dt7cB/JezZswcvvPACNm3aNODjdXV1aG1txRNPPIFFixbh448/xvXXX48bbrgBu3fvHvA1ZrMZRqOxzy3QjO1pvO09QC1QOHYIseGWfNmC7HgoBODoWSMqzrW79NqWDouz/+Umzl6hAOHRXUImkwmrV6/Gpk2bEBMTM+BzbDYbAOC6667Dfffdh6lTp+Khhx7Cd77zHWzcuHHA12zYsAHh4eHOW2pq4P2DVSkVzl/YgdbH4igJcUsz+bLoUC0uyXSUhVxbZdlyuBrmbhvGx4diamqEB6Ijkh+XEpaYmBgolUoYDIY+9xsMBiQk9K+hlpWVoby8HEuXLoVKpYJKpcLLL7+MLVu2QKVSoaysDDExMVCpVMjOzu7z2okTJw66S2j9+vVoaWlx3iorK135MvxGIA6QqzeZUWcyQxCArASO5Cff5twt5GIfy5s95aCbeNAhBRCXEhaNRoMZM2Zgx44dzvtsNht27NiBOXPm9Ht+VlYWCgsLUVBQ4LwtW7YM8+fPR0FBAVJTU6HRaDBr1iycOHGiz2tLSkqQnp4+YBxarRZ6vb7PLRDlBOBOIcfqypjoENbtyectnBQPQQAOVzajqml4ZaHiGiOOVLVArRRw/TQedEiBw+Wf+OvWrcOaNWswc+ZMXHzxxXjmmWfQ1taGtWvXAgBuu+02JCcnY8OGDdDpdMjJyenz+oiICADoc/8DDzyAlStX4vLLL8f8+fOxbds2vP/++9i1a9fIv7IA4FhhOXbW3nirCIAdM46EZSLLQeQH4sJ0mJURhf2nG7HtaC1+MK//Lspvc0y2zZsYz3O0KKC4nLCsXLkS9fX1eOSRR1BbW4upU6di27ZtzkbciooKKBSutcZcf/312LhxIzZs2ICf/vSnmDBhAt566y3MnTvX1fACyri4UGhVCpjM3TjT2I4xMf4/k4Q7hMjfLMlJwP7Tjdg6jITF3G3FuwX2gw45e4UCjSCKos/viTUajQgPD0dLS0vAlYeWP/clCiqb8ddbpmHZlCSpw/G4BX/ejRJDK/55+yzMz+KcHvJ9BmMnZv/eXmb/ev3VSAgffGDmB0eqcc9/DiFBr8OXD13FOUTk81z5/c2zhHxcTnLgjOjvtFhRVm/fws0dQuQv4vU6zEyPBABsu8BuIUc56LszUpisUMBhwuLjnCP6q/w/YSkxmGC1iYgK0SAujLV78h/D2S1U1dSOPScbAHD2CgUmJiw+zjmiv7rF7yfeFvfqX+FWTvIni3LsYyG+KW9EnbFzwOf8N78KogjMyYxGWnSwN8MjkgUmLD5ufHwY9DoVTJ3d2FUy9AGUvs4x4ZblIPI3yRFBmJoaAVEEth/rv8pis4l4s+egw5VstqUAxYTFx6mVCtx8cRoA4J9flksbjIc5dghNTOTAOPI/jgMMPyrsn7B8VXYOZ5s7EKZTOVdjiAINExY/cNucdCgE4IvSBpQYTFKH4xE2m4jiGvvXlp0YLnE0RO63OMfex7Lv9Dk0tJr7PPZ6z2Tb66YmQadWej02IjlgwuIHUiKDsSDb/leXv66yVDV1oNXcDY1KgcxY/583Q4EnNSoYucnhsInAx8fOH3/S3N7lLBOtnJkmVXhEkmPC4ifWXpYBAHjnUBWa27ukDcYDHOWg8fGhUCv5bUv+aXFPWaj3YYjvHjqLrm4bJibqnWMMiAIRf/L7iYvHRGFSkh6dFhte2+9/h0Fywi0FAkdZ6Kuyc2hqs//h8Yaj2XZmCnfHUUBjwuInBEHA2svGAABe3lsOi9UmcUTu5dwhxISF/NiYmBBMTNTDahPxSZEBR8+2oKjGCI1Sgeum8qBDCmxMWPzI0imJiAnVoKalc8Ctkb7MeeghExbyc0t6dgF9dLTGOdl2waR4RIZopAyLSHJMWPyIVqXE92anA/Cv5tuWdgvONncA4CnN5P8cU2+/PNngPOiQs1eImLD4nVsvSYNaKSD/TBMOVzZLHY5bOPpXUiKDoNepJY6GyLPGxoViQnwYLFYRps5uJEcE4bKLYqQOi0hyTFj8TFyYDksn209t/ueXpyWOxj2K2XBLAcaxWwiwH3So4EGHRExY/JGj+fbDwppBzyXxJc4dQiwHUYBY0lMWEgTgxpkpEkdDJA9MWPxQbko4ZqZHwmIV8crXZ6QOZ9QcO4TYcEuBYnx8GJ5ckYtnVk5FSiQPOiQCmLD4re/Pta+yvLqvAp0Wq8TRjFxXtw0n61oBsCREgWXlrDRuZSbqhQmLn1qQHY/kiCCca+vClsPVUoczYmX1reiy2hCmUyElMkjqcIiISCJMWPyUSqnA6jnntziLoihxRCPTe/4Kp3wSEQUuJix+7OZZqdCpFSiuMWLf6UapwxkRTrglIiKACYtfiwjWYMV0+w4DX93izDOEiIgIYMLi9xynOH9cZEBlY7u0wbhIFMXzM1i4pZmIKKAxYfFzY+PCMG9cDEQReOmrcqnDcUmtsRNN7RaoFALGxoVKHQ4REUmICUsA+H7PILnXD1SizdwtcTTD5+hfuSg2FDq1UuJoiIhISkxYAsAV42ORGRMCU2c33jpYJXU4w8ZyEBEROTBhCQAKhYDbe3pZ/vllOWw239jizIZbIiJyYMISIFZMT0GYToXTDW3YXVIvdTjDUlxjAsCR/ERExIQlYIRoVVg5MxUA8KIPbHFuNXej/FwbAGBiYpjE0RARkdSYsASQNZdmQCEAX5Q2oNRgkjqcIZ2oNUIUgQS9DtGhWqnDISIiiTFhCSCpUcG4JjseAPBPmW9xLnKWg7i6QkRETFgCztqeLc5vH6xCc3uXxNEMzjmSnzuEiIgITFgCzuwxUZiYqEenxYbN31RKHc6ginodekhERMSEJcAIgoDv92xxfvmrcnRbbdIGNACrTcSJWm5pJiKi85iwBKClU5IQHaJBdUsnth8zSB1OP6cb2tBpsSFYo0R6dIjU4RARkQwwYQlAOrUSq2anAZDnKc6OctCEhDAoFYLE0RARkRwwYQlQt16SDrVSwIEzTThS1Sx1OH0Uc8ItERF9CxOWABWn1+E7k5MA2Mf1ywl3CBER0bcxYQlga3uabz84Uo06Y6e0wfRSzB1CRET0LUxYAtjklAjMSI+ExSrilX0VUocDAKg3mVFnMkMQgKwEDo0jIiI7JiwBzrHK8p99Z9BpsUobDM6vroyJCUGwRiVxNEREJBdMWALcokkJSArXoaG1C+8frpY6HJaDiIhoQExYApxKqcDqORkA7M23oihKGk8RdwgREdEAmLAQbrk4FTq1AkU1Ruw/3ShpLM4dQkxYiIioFyYshIhgDa6flgJA2i3OnRYrTjW0AeCWZiIi6osJCwGA83yhj4tqUdnYLkkMJQYTrDYR0SEaxIVpJYmBiIjkiQkLAQDGxYdh3rgY2ETg5b3lksTQu+FWEDiSn4iIzmPCQk6OLc6bv6lEm7nb65/PCbdERDQYJizkdOX4OIyJCYGpsxtvHazy+udzhxAREQ2GCQs5KRQCbr80AwDwry/LYbN5b4uzzSaiuMYEgDNYiIioPyYs1MeKGSkI06pwqqENu0vrvfa5VU0daDV3Q6NSIDM2xGufS0REvoEJC/URqlXhplmpALy7xbmopgUAMCE+DGolvy2JiKgv/magfm6/NAMKAfi8pB4n60xe+cwiZzmIBx4SEVF/TFion9SoYORNjAfgvVUWTrglIqKhMGGhAa29bAwA4O2DZ9HSbvH45/HQQyIiGgoTFhrQJZlRyEoIQ4fFis3fVHj0s1raLTjb3AEAmMgZLERENAAmLDQgQRDw/bn2VZaXvipHt9Xmsc9yzF9JjQqCXqf22OcQEZHvYsJCg1o2JQnRIRpUt3Ti4yKDxz7HWQ5K4OoKERENjAkLDUqnVuJ7s9MAAP/88rTHPsc54ZblICIiGgQTFhrSrZekQ6UQ8E15EwqrWjzyGdwhREREF8KEhYYUr9fhO5MTAXhmlaWr24aTda0AuEOIiIgGN6KE5bnnnkNGRgZ0Oh1mz56N/fv3D+t1mzdvhiAIWL58eZ/7b7/9dgiC0Oe2aNGikYRGHuDY4vz+kWrUmTrd+t5l9a3ostoQplMhJTLIre9NRET+w+WE5fXXX8e6devw6KOP4uDBg5gyZQoWLlyIurq6IV9XXl6O+++/H/PmzRvw8UWLFqGmpsZ5e+2111wNjTxkSmoEpqdFwGIV8erX7t3i7CgHTUzUQxAEt743ERH5D5cTlqeffhp33nkn1q5di+zsbGzcuBHBwcF48cUXB32N1WrFqlWr8NhjjyEzM3PA52i1WiQkJDhvkZGRroZGHuRYZXl13xmYu61ue1/HDiH2rxAR0VBcSli6urqQn5+PvLy882+gUCAvLw979+4d9HWPP/444uLicMcddwz6nF27diEuLg4TJkzA3XffjXPnzrkSGnnYopwEJIbr0NDahfcP17jtfblDiIiIhsOlhKWhoQFWqxXx8fF97o+Pj0dtbe2Ar9mzZw9eeOEFbNq0adD3XbRoEV5++WXs2LEDTz75JHbv3o3FixfDah34L3mz2Qyj0djnRp6lViqwek46AHvzrSiKo35PURS5wkJERMPi0V1CJpMJq1evxqZNmxATEzPo826++WYsW7YMubm5WL58OT744AN888032LVr14DP37BhA8LDw5231NRUD30F1Nsts9KgUytwrNqIb8qbRv1+tcZONLVboFIIGBsX6oYIiYjIX7mUsMTExECpVMJg6Dv11GAwICEhod/zy8rKUF5ejqVLl0KlUkGlUuHll1/Gli1boFKpUFZWNuDnZGZmIiYmBidPnhzw8fXr16OlpcV5q6ysdOXLoBGKDNHg+mnJAIAX94x+i7Oj4XZsXCh0auWo34+IiPyXSwmLRqPBjBkzsGPHDud9NpsNO3bswJw5c/o9PysrC4WFhSgoKHDeli1bhvnz56OgoGDQlZGqqiqcO3cOiYmJAz6u1Wqh1+v73Mg7HM23HxfVorKxfVTvxROaiYhouFSuvmDdunVYs2YNZs6ciYsvvhjPPPMM2trasHbtWgDAbbfdhuTkZGzYsAE6nQ45OTl9Xh8REQEAzvtbW1vx2GOPYcWKFUhISEBZWRl+8YtfYOzYsVi4cOEovzxyt/HxYZg7NgZ7Tjbg31+fwS+XTBzxexWxf4WIiIbJ5YRl5cqVqK+vxyOPPILa2lpMnToV27ZtczbiVlRUQKEY/sKNUqnEkSNH8NJLL6G5uRlJSUlYsGABfvOb30Cr1boaHnnB2ssysOdkAzbvr8C9V49DiNblbyMAvUbyc4cQERFdgCC6Y7uHxIxGI8LDw9HS0sLykBfYbCKu+tMulJ9rx2+um4TVczJcfo9Wczdy/3c7RBE4+PA1iArRuD9QIiKSNVd+f/MsIXKZQiHg9kszAAD//KocNpvrOe+JWiNEEUjQ65isEBHRBTFhoRH57sxUhGlVOFXfhs9L611+fVGNCQAwMTHM3aEREZEfYsJCIxKqVeHGmfZdXv/8stzl17N/hYiIXMGEhUbs9kszIAjA7pJ6nKxrdem153cIhXsiNCIi8jNMWGjE0qKDkTfRvjvsX18Nf5Cc1SbiRK1jBgtLQkREdGFMWGhU1l6WAQB4K/8sWtotw3rN6YY2dFpsCNYokR4d4sHoiIjIXzBhoVGZkxmNrIQwdFis2PxNxbBe4ygHZSWEQakQPBkeERH5CSYsNCqCIDhXWV7eewbdVtsFX8OR/ERE5ComLDRq101NRlSIBmebO/BJkeGCz+cOISIichUTFho1nVqJ712cBmB4W5yLeYYQERG5iAkLucXqOelQKQTsL2/E0bMtgz6v3mRGnckMQQAmJHCHEBERDQ8TFnKLeL0OS3ITAQAvfjn4FmfH6sqYmBAEa0Z2aCIREQUeJizkNt+fOwYA8MHhGtSbzAM+hw23REQ0EkxYyG2mpkZgWloEuqw2vLrvzIDPKWL/ChERjQATFnKrtZfZV1le+foMzN3Wfo9zhxAREY0EExZyq8U5CUjQ69DQ2oUPDtf0eazTYsWphjYAXGEhIiLXMGEht1IrFVg9Jx0A8M+vTkMURedjJQYTrDYR0SEaxIVppQqRiIh8EBMWcrvvXZwGrUqBo2eNOHCmyXl/73KQIHAkPxERDR8TFnK7yBANrp+WDAB4cc/5Lc7cIURERCPFhIU84vae84W2H6tFVVM7AO4QIiKikWPCQh6RlaDHZWOjYROBf+89A5tNRHGNCQB3CBERkeuYsJDHrL3UvsX5tf0VKKkzodXcDY1KgcyYEIkjIyIiX8OEhTzmqqw4pEcHw9jZjd99WAwAmBAfBpWS33ZEROQa/uYgj1EoBKyZkwEA+KK0AQAwMZEHHhIRkeuYsJBH3TgzBaHa84ccsuGWiIhGggkLeVSYTo0bZ6Y4/zs7KVzCaIiIyFcxYSGPu/3SDCgEQKNSIIslISIiGgHVhZ9CNDrp0SF4+fuzIQiAXqeWOhwiIvJBTFjIK+aOi5E6BCIi8mEsCREREZHsMWEhIiIi2WPCQkRERLLHhIWIiIhkjwkLERERyR4TFiIiIpI9JixEREQke0xYiIiISPaYsBAREZHsMWEhIiIi2WPCQkRERLLHhIWIiIhkjwkLERERyZ5fnNYsiiIAwGg0ShwJERERDZfj97bj9/hQ/CJhMZlMAIDU1FSJIyEiIiJXmUwmhIeHD/kcQRxOWiNzNpsN1dXVCAsLgyAIUofjdUajEampqaisrIRer5c6HJ/F6+gevI7uwevoHryO7uGp6yiKIkwmE5KSkqBQDN2l4hcrLAqFAikpKVKHITm9Xs9/kG7A6+gevI7uwevoHryO7uGJ63ihlRUHNt0SERGR7DFhISIiItljwuIHtFotHn30UWi1WqlD8Wm8ju7B6+gevI7uwevoHnK4jn7RdEtERET+jSssREREJHtMWIiIiEj2mLAQERGR7DFhISIiItljwuJDzp49i1tvvRXR0dEICgpCbm4uDhw44HxcFEU88sgjSExMRFBQEPLy8lBaWiphxPJjtVrx8MMPY8yYMQgKCsJFF12E3/zmN33OseB17O/zzz/H0qVLkZSUBEEQ8O677/Z5fDjXrLGxEatWrYJer0dERATuuOMOtLa2evGrkN5Q19FiseDBBx9Ebm4uQkJCkJSUhNtuuw3V1dV93oPX8cLfj73dddddEAQBzzzzTJ/7eR2Hdx2Li4uxbNkyhIeHIyQkBLNmzUJFRYXz8c7OTvz4xz9GdHQ0QkNDsWLFChgMBo/Ey4TFRzQ1NeGyyy6DWq3G1q1bUVRUhD/96U+IjIx0Puepp57CX//6V2zcuBH79u1DSEgIFi5ciM7OTgkjl5cnn3wSzz//PP72t7+huLgYTz75JJ566ik8++yzzufwOvbX1taGKVOm4Lnnnhvw8eFcs1WrVuHYsWP45JNP8MEHH+Dzzz/HD3/4Q299CbIw1HVsb2/HwYMH8fDDD+PgwYN4++23ceLECSxbtqzP83gdL/z96PDOO+/g66+/RlJSUr/HeB0vfB3Lysowd+5cZGVlYdeuXThy5Agefvhh6HQ653Puu+8+vP/++3jzzTexe/duVFdX44YbbvBMwCL5hAcffFCcO3fuoI/bbDYxISFB/MMf/uC8r7m5WdRqteJrr73mjRB9wrXXXit+//vf73PfDTfcIK5atUoURV7H4QAgvvPOO87/Hs41KyoqEgGI33zzjfM5W7duFQVBEM+ePeu12OXk29dxIPv37xcBiGfOnBFFkddxIINdx6qqKjE5OVk8evSomJ6eLv75z392Psbr2N9A13HlypXirbfeOuhrmpubRbVaLb755pvO+4qLi0UA4t69e90eI1dYfMSWLVswc+ZM3HjjjYiLi8O0adOwadMm5+OnT59GbW0t8vLynPeFh4dj9uzZ2Lt3rxQhy9Kll16KHTt2oKSkBABw+PBh7NmzB4sXLwbA6zgSw7lme/fuRUREBGbOnOl8Tl5eHhQKBfbt2+f1mH1FS0sLBEFAREQEAF7H4bLZbFi9ejUeeOABTJo0qd/jvI4XZrPZ8OGHH2L8+PFYuHAh4uLiMHv27D5lo/z8fFgslj7/9rOyspCWluaRn5dMWHzEqVOn8Pzzz2PcuHHYvn077r77bvz0pz/FSy+9BACora0FAMTHx/d5XXx8vPMxAh566CHcfPPNyMrKglqtxrRp0/Czn/0Mq1atAsDrOBLDuWa1tbWIi4vr87hKpUJUVBSv6yA6Ozvx4IMP4pZbbnEeNsfrODxPPvkkVCoVfvrTnw74OK/jhdXV1aG1tRVPPPEEFi1ahI8//hjXX389brjhBuzevRuA/TpqNBpnQu3gqZ+XfnFacyCw2WyYOXMmfv/73wMApk2bhqNHj2Ljxo1Ys2aNxNH5jjfeeAOvvvoq/vOf/2DSpEkoKCjAz372MyQlJfE6kmxYLBbcdNNNEEURzz//vNTh+JT8/Hz85S9/wcGDByEIgtTh+CybzQYAuO6663DfffcBAKZOnYqvvvoKGzduxBVXXOH1mLjC4iMSExORnZ3d576JEyc6u7UTEhIAoF93tsFgcD5GwAMPPOBcZcnNzcXq1atx3333YcOGDQB4HUdiONcsISEBdXV1fR7v7u5GY2Mjr+u3OJKVM2fO4JNPPnGurgC8jsPxxRdfoK6uDmlpaVCpVFCpVDhz5gx+/vOfIyMjAwCv43DExMRApVJd8PdOV1cXmpub+zzHUz8vmbD4iMsuuwwnTpzoc19JSQnS09MBAGPGjEFCQgJ27NjhfNxoNGLfvn2YM2eOV2OVs/b2digUfb/tlUql868JXkfXDeeazZkzB83NzcjPz3c+57PPPoPNZsPs2bO9HrNcOZKV0tJSfPrpp4iOju7zOK/jha1evRpHjhxBQUGB85aUlIQHHngA27dvB8DrOBwajQazZs0a8vfOjBkzoFar+/zbP3HiBCoqKjzz89LtbbzkEfv37xdVKpX4u9/9TiwtLRVfffVVMTg4WHzllVecz3niiSfEiIgI8b333hOPHDkiXnfddeKYMWPEjo4OCSOXlzVr1ojJycniBx98IJ4+fVp8++23xZiYGPEXv/iF8zm8jv2ZTCbx0KFD4qFDh0QA4tNPPy0eOnTIuXtlONds0aJF4rRp08R9+/aJe/bsEceNGyfecsstUn1JkhjqOnZ1dYnLli0TU1JSxIKCArGmpsZ5M5vNzvfgdbzw9+O3fXuXkCjyOoriha/j22+/LarVavEf//iHWFpaKj777LOiUqkUv/jiC+d73HXXXWJaWpr42WefiQcOHBDnzJkjzpkzxyPxMmHxIe+//76Yk5MjarVaMSsrS/zHP/7R53GbzSY+/PDDYnx8vKjVasWrr75aPHHihETRypPRaBTvvfdeMS0tTdTpdGJmZqb4q1/9qs8vBF7H/nbu3CkC6Hdbs2aNKIrDu2bnzp0Tb7nlFjE0NFTU6/Xi2rVrRZPJJMFXI52hruPp06cHfAyAuHPnTud78Dpe+Pvx2wZKWHgdh3cdX3jhBXHs2LGiTqcTp0yZIr777rt93qOjo0P80Y9+JEZGRorBwcHi9ddfL9bU1HgkXkEUe434JCIiIpIh9rAQERGR7DFhISIiItljwkJERESyx4SFiIiIZI8JCxEREckeExYiIiKSPSYsREREJHtMWIiIiEj2mLAQERGR7DFhISIiItljwkJERESyx4SFiIiIZO//A5G9BPOT0AAaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(param_range, gs.cv_results_[\"mean_test_score\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "37b571c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error: 5.36\n",
      "median_absolute_error: 3.99\n",
      "r2_score: 0.82\n"
     ]
    }
   ],
   "source": [
    "reg.fit(train_x, train_y)\n",
    "test_model(reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5861977e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(n_estimators=80)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(n_estimators=80)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(n_estimators=80)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gr_80 = GradientBoostingRegressor(n_estimators=80)\n",
    "gr_80.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3385fca6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error: 6.64\n",
      "median_absolute_error: 4.44\n",
      "r2_score: 0.66\n"
     ]
    }
   ],
   "source": [
    "test_model(gr_80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "07348f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=None, test_size=None, train_size=None),\n",
       "             estimator=GradientBoostingRegressor(), n_jobs=-1,\n",
       "             param_grid=[{&#x27;n_estimators&#x27;: array([ 50,  60,  70,  80,  90, 100, 110, 120, 130, 140, 150, 160])}],\n",
       "             return_train_score=True, scoring=&#x27;r2&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=None, test_size=None, train_size=None),\n",
       "             estimator=GradientBoostingRegressor(), n_jobs=-1,\n",
       "             param_grid=[{&#x27;n_estimators&#x27;: array([ 50,  60,  70,  80,  90, 100, 110, 120, 130, 140, 150, 160])}],\n",
       "             return_train_score=True, scoring=&#x27;r2&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=ShuffleSplit(n_splits=10, random_state=None, test_size=None, train_size=None),\n",
       "             estimator=GradientBoostingRegressor(), n_jobs=-1,\n",
       "             param_grid=[{'n_estimators': array([ 50,  60,  70,  80,  90, 100, 110, 120, 130, 140, 150, 160])}],\n",
       "             return_train_score=True, scoring='r2')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs = GridSearchCV(GradientBoostingRegressor(), tuned_parameters,\n",
    "                  cv=ShuffleSplit(n_splits=10), scoring=\"r2\",\n",
    "                  return_train_score=True, n_jobs=-1)\n",
    "gs.fit(data_x, data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "775ff086",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = gs.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2e4900fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(param_range, gs.cv_results_[\"mean_train_score\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c29f7db5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(param_range, gs.cv_results_[\"mean_test_score\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f2e50fba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_absolute_error: 6.75\n",
      "median_absolute_error: 4.11\n",
      "r2_score: 0.64\n"
     ]
    }
   ],
   "source": [
    "reg.fit(train_x, train_y)\n",
    "test_model(reg)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
